A multi-objective surrogate-based optimization of the crashworthiness of a hybrid impact absorber

Costas, Miguel; Díaz, J.; Romera, Luis; Hernández, Santiago

doi:10.1016/j.ijmecsci.2014.07.002

Cited by 67 publications

(29 citation statements)

References 27 publications

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“…The computation time for each simulation was between 20-50 minutes. Given the good results found in [29,40], a multivariate adaptive regression splines (MARS) model [41,42] was fitted to the sampling results with acceptable likelihood estimators, provided in Section 4 for each optimization case. A description of this metamodel is provided in [43].…”

Section: Problem Statement and Methodologymentioning

confidence: 99%

“…The inner reinforcement of metal tubes with composite structures has been recently studied by Costas et al [28,29], motivating the present investigation. The beneficial contribution of an inner GFRP skeleton to the crashworthiness of a steel tube was stated, but it was also observed that the efficiency of the design could be significantly enhanced with some kind of constraining or confinement system which prevented the GFRP parts from falling apart after incipient fracture.…”

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confidence: 90%

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Static crushing of aluminium tubes filled with PET foam and a GFRP skeleton. Numerical modelling and multiobjective optimization

Costas

Morin

Langseth

et al. 2017

International Journal of Mechanical Sciences

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This investigation focuses on the multiobjective optimization of a crash box subjected to static loading by using a validated numerical model and an analytical approach. The crash box is made with a unique combination of three materials: an aluminium tube filled with polyethylene terephthalate (PET) foam and a glass-fibre reinforced polymer (GFRP) skeleton. A finite element model was calibrated based on the results obtained in a material testing campaign using appropriate constitutive equations. A J2-plasticity model was used for the material behaviour of the aluminium alloy, and the PET foam was modelled using Deshpande and Fleck's model. Regarding the short-fibres GFRP, a Voce plasticity model was fitted to the experimental data. After a successful validation of the finite element model, the filled aluminium tube was subjected to a structural optimization to achieve the best crash performance. Three relevant design variables were selected: the thickness of the outer aluminium cylinder, the thickness of the GFRP and the density of the PET foam, the last being related to the crushing strength of the foam. Given the high computational cost of each finite element model, a multi-adaptive regression splines metamodel was fitted to a large-scale sampling. Optimum pairs were obtained for the absorbed energy, the specific energy absorption, the peak load and the mass of the com- the component over the design region selected for the optimization, and was also used for its optimization with satisfactory results.

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Section: Problem Statement and Methodologymentioning

confidence: 99%

mentioning

confidence: 90%

Static crushing of aluminium tubes filled with PET foam and a GFRP skeleton. Numerical modelling and multiobjective optimization

Costas

Morin

Langseth

et al. 2017

International Journal of Mechanical Sciences

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“…The Specific Energy Absorption (SEA) and the Load Ratio (LR), whose responses were approximated by multiple types of surrogate models, have been considered as contrasting objective functions. The results show improvements in both functions by almost 50% compared to the original design (Costas et al, 2014). Multi-cell square tubes under dynamic oblique impact loading have been studied by Trong Nhan Tran et.…”

Section: Latin American Journal Of Solids and Structures 13 (2016) 25mentioning

confidence: 95%

Developing and Multi-Objective Optimization of a Combined Energy Absorber Structure Using Polynomial Neural Networks and Evolutionary Algorithms

Najibi

Shojaeefard

Yeganeh

2016

Lat. Am. j. solids struct.

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In this study a newly developed thin-walled structure with the combination of circular and square sections is investigated in term of crashworthiness. The results of the experimental tests are utilized to validate the Abaqus/Explicit TM finite element simulations and analysis of the crush phenomenon. Three polynomial metamodels based on the evolved group method of data handling (GMDH) neural networks are employed to simply represent the specific energy absorption (SEA), the initial peak crushing load (P1) and the secondary peak crushing load (P2) with respect to the geometrical variables. The training and testing data are extracted from the finite element analysis. The modified genetic algorithm NSGA-II, is used in multi-objective optimisation of the specific energy absorption, primary and secondary peak crushing load according to the geometrical variables. Finally, in each optimisation process, the optimal section energy absorptions are compared with the results of the finite element analysis. The nearest to ideal point and TOPSIS optimisation methods are applied to choose the optimal points.

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“…The surrogate model method has been properly utilized to simplify practical models in design problems [33,34]. A surrogateassisted evolution strategy was proposed and applied to multiobjective optimization [35].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Algorithm for Solving Multiobjective Optimization Problems with Engineering Applications

Fan

Yoshino

et al. 2018

Mathematical Problems in Engineering

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An effective hybrid algorithm is proposed for solving multiobjective optimization engineering problems with inequality constraints. The weighted sum technique and BFGS quasi-Newton's method are combined to determine a descent search direction for solving multiobjective optimization problems. To improve the computational efficiency and maintain rapid convergence, a cautious BFGS iterative format is utilized to approximate the Hessian matrices of the objective functions instead of evaluating them exactly. The effectiveness of the proposed algorithm is demonstrated through a comparison study, which is based on numerical examples. Meanwhile, we propose an effective multiobjective optimization strategy based on the algorithm in conjunction with the surrogate model method. This proposed strategy has been applied to the crashworthiness design of the primary energy absorption device's crash box structure and front rail under low-speed frontal collision. The optimal results demonstrate that the proposed methodology is promising in solving multiobjective optimization problems in engineering practice.

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A multi-objective surrogate-based optimization of the crashworthiness of a hybrid impact absorber

Cited by 67 publications

References 27 publications

Static crushing of aluminium tubes filled with PET foam and a GFRP skeleton. Numerical modelling and multiobjective optimization

Static crushing of aluminium tubes filled with PET foam and a GFRP skeleton. Numerical modelling and multiobjective optimization

Developing and Multi-Objective Optimization of a Combined Energy Absorber Structure Using Polynomial Neural Networks and Evolutionary Algorithms

A Novel Hybrid Algorithm for Solving Multiobjective Optimization Problems with Engineering Applications

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